Open AccessGA-ARIMA Model-Based Analysis of Arrival time at Bus Stop
Author(s) -
Xiang Guo,
Deyong Guan,
Yu Xue
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/587/1/012050
Subject(s) - autoregressive integrated moving average , arrival time , computer science , global positioning system , time series , hotspot (geology) , public transport , traffic congestion , real time computing , genetic algorithm , transport engineering , engineering , telecommunications , machine learning , geophysics , geology
Background With the ever-changing urban development and a growing number of vehicles, the road congestion problem has become a hotspot issue at present. As an important constituent part of road traffic system, public traffic system has become an effective force relieving the traffic congestion problem. Objective To explore how to better reduce the prediction error of arrival time at bus stop on basis of actual GPS bus data. Method The arrival time at bus stop was analyzed based on GPS data, a time series optimization model based on genetic algorithm was established, followed by the prediction error analysis of arrival time at bus stop. Result The prediction result of GA-ARIMA model is superior to that of the traditional time series model, which indicates the effectiveness of this model.